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The spin coating method was employed to fabricate Yb-doped ZnO thin films at 0, 3, 5, 7, and 9 at.% over a glass substrate at low temperature. X-ray diffraction analysis revealed that the hexagonal wurtzite structure was retained even at high doping contents. With the incorporation of Yb+3 ions, a slight decrease in the lattice parameters and crystallite size was observed as the ytterbium content increased. X-ray photoelectron spectroscopy confirmed the presence of ytterbium in the doped ZnO films, and the oxidation state of ytterbium was 3+ for all the samples. Morphological studies revealed a surface microstructure formed by micro islands, which tended to be denser as the ytterbium content increased. Optical transmittance was observed at approximately 75–85%, a blueshift was observed, and consequently, an increase in the bandgap, which varies from 3.0 to 3.2 eV, was observed. The refractive index and extinction coefficient decreased as the ytterbium dopant concentration increased. The photoluminescence results exhibited a strong ultraviolet emission, allowing the use of these thin films in optoelectronic applications.

Background Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. Methods To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. Results The classifier with the highest metrics was the four antibody-based Perfecto–Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P

Nanostructured Zn1-xYbxO (0.0 ≤ x ≤ 0.1) powders were prepared by the solution method using polyvinyl alcohol (PVA) and sucrose. The effect of the ytterbium doping content on the structural, morphological, optical and antimicrobial properties was analyzed. X-ray diffraction (XRD) analysis revealed that the hexagonal wurtzite structure was retained, and no secondary phases due to doping were observed. The crystallite size was under 20 nm for all the Zn1-xYbxO (0.0 ≤ x ≤ 0.1) powders. The optical band gap was calculated, and the results revealed that this value increased with the ytterbium content, and the Eg values varied from 3.06 to 3.10 eV. The surface chemistry of the powders was analyzed using X-ray photoelectron spectroscopy (XPS), and the results confirmed the oxidation state of ytterbium as 3+ for all the samples. Zn1-xYbxO (0.0 ≤ x ≤ 0.1) nanoparticles were tested as antimicrobial agents against Staphylococcus aureus and Escherichia coli, resulting in a potential antimicrobial effect at most of the tested concentrations. These results were used in an artificial neural network (ANN). The results showed that it is possible to generate a model capable of forecasting the absorbance with good precision (error of 1–2%).

Antimicrobial activity of the Zn1-xErxO (0, 1, 5, 10 at.%) nanoparticles were tested against Staphylococcus aureus and Escherichia coli. The nanoparticles were successfully synthesized by wet chemical route, where polyvinyl alcohol and sucrose were used. The influence of erbium content in structural, optical, and antimicrobial activity was analyzed. The average crystallite size is under 15 nm for all the samples according to X-ray diffraction results, and no secondary phases were observed even at high erbium content. Optical results exhibit a blue shift in the ultra-violet region. The X-ray photoelectron spectroscopy analysis confirmed the variations of Zn/O ratio, together with particles size and band gap are key factor in antimicrobial properties. The microbiological essays exhibit to these nanoparticles as a high potential agent with antibacterial activity versus S. aureus, with lower impact in E. coli. The absorbance results of these assays were used in two theoretical approaches. At first, Gompertz model used in the regression analysis showed the best fit for bacterial growth. Additionally, an artificial neural network was trained to forecast the result of new experiments, showing a good performance.

Brain–computer interfaces (BCI) rely on classification algorithms to detect the patterns of the brain signals that encode the mental task performed by the user. Therefore, robust and reliable classification techniques should be developed and evaluated to recognize the user's mental task with high accuracy. This paper proposes the use of the novel dendrite morphological neural networks (DMNN) for the recognition of voluntary movements from electroencephalographic (EEG) signals. This technique was evaluated with two studies. The first aimed to evaluate the performance of DMNN in the recognition of motor execution and motor imagery tasks and to carry out a systematic comparison with support vector machine (SVM) and linear discriminant analysis (LDA) which are the two classifiers mostly used in BCI systems. EEG signals from twelve healthy students were recorded during a cue-based hand motor execution and imagery experiment. The results showed that DMNN provided decoding accuracies of 80% for motor execution and 77% for motor imagery, which were significantly different than the chance level (p

This Workshop on the Algorithmic Foundations of Robotics (WAFR) Special Issue of the IEEE Transactions on Automation Science and Engineering (T-ASE) brings together eight extended articles from the thirteenth WAFR. While these eight articles span several application domains, they demonstrate advances in automation through algorithmic development and analysis. Nomination to this Special Issue was done in coordination with the entire program committee and guest edited by the four WAFR Co-Chairs. The articles in this Special Issue highlight cutting-edge research in general tools for motion planning, learning, control, manipulation, sensor-based planning, and robotic design.

The segmentation of digital images is one of the most important steps in an image processing or computer vision system. It helps to classify the pixels in different regions according to their intensity level. Several segmentation techniques have been proposed, and some of them use complex operators. The techniques based on thresholding are the easiest to implement; the problem is to select correctly the best threshold that divides the pixels. An interesting method to choose the best thresholds is the minimum cross entropy (MCET), which provides excellent results for bi-level thresholding. Nevertheless, the extension of the segmentation problem into multiple thresholds increases significantly the computational effort required to find optimal threshold values. Each new threshold adds complexity to the formulation of the problem. Classic methods for image thresholding perform extensive searches, while new approaches take advantage of heuristics to reduce the search. Evolutionary algorithms use heuristics to optimize criteria over a finite number of iterations. The correct selection of an evolutionary algorithm to minimize the MCET directly impacts the performance of the method. Current approaches take a large number of iterations to converge and a high rate of MCET function evaluations. The electromagnetism-like optimization (EMO) algorithm is an evolutionary technique which emulates the attraction–repulsion mechanism among charges for evolving the individuals of a population. Such technique requires only a small number of evaluations to find the optimum. This paper proposes the use of EMO to search for optimal threshold values by minimizing the cross entropy function while reducing the amount of iterations and function evaluations. The approach is tested on a set of benchmark images to demonstrate that is able to improve the convergence and velocity; additionally, it is compared with similar state-of-the-art optimization approaches.

DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. The main idea is to retain only the genes that are the most influential in the classification of the disease. In this paper, a new methodology based on Principal Component Analysis and Logistics Regression is proposed. Our method enables the selection of particular genes that are relevant for classification. Experiments were run using eight different classifiers on two benchmark datasets: Leukemia and Lymphoma. The results show that our method not only reduces the number of required attributes, but also increase the classification accuracy in more than 10% in all the cases we tested.

Deception is considered a psychological process by which one individual deliberately attempts to convince another person to accept as true what the liar knows to be false. This paper presents the use of functional near-infrared spectroscopy for deception detection. This technique measures hemodynamic variations in the cortical regions induced by neural activations. The experimental setup involved a mock theft paradigm with ten subjects, where the subjects responded to a set of questions, with each of their answers belonging to one of three categories: Induced Lies, Induced Truths, and Non-Induced responses. The relative changes of the hemodynamic activity in the subject's prefrontal cortex were recorded during the experiment. From this data, the changes in blood volume were derived and represented as false color topograms. Finally, a human evaluator used these topograms as a guide to classify each answer into one of the three categories. His performance was compared with that of a support vector machine (SVM) classifier in terms of accuracy, specificity, and sensitivity. The human evaluator achieved an accuracy of 84.33 % in a tri-class problem and 92 % in a bi-class problem (induced vs. non-induced responses). In comparison, the SVM classifier correctly classified 95.63 % of the answers in a tri-class problem using cross-validation for the selection of the best features. These results suggest a tradeoff between accuracy and computational burden. In other words, it is possible for an interviewer to classify each response by only looking at the topogram of the hemodynamic activity, but at the cost of reduced prediction accuracy.

Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physicians. It has been proposed to create automated systems that can perform such task with little intervention from humans, with partial success. In this work, we report advances in such endeavor, by using a Lattice Neural Network with Dendritic Processing (LNNDP). We report results using several metrics, and compare against well known methods such as Support Vector Machines (SVM) and Multilayer Perceptrons (MLP). Our proposal shows better performance than other approaches reported in the literature. An additional advantage is that unlike those other tools, LNNDP requires no parameters, and it automatically constructs its structure to solve a particular problem. The proposed methodology requires four steps: (1) Pre-processing, (2) Feature computation, (3) Classification and (4) Post-processing. The Hotelling T2 control chart was used to reduce the dimensionality of the feature vector, from 7 that were used before to 5 in this work. The experiments were run on images of DRIVE and STARE databases. The results show that on average, F1-Score is better in LNNDP, compared with SVM and MLP implementations. Same improvement is observed for MCC and the accuracy. HighlightsFirst implementation of a Lattice Neural Network with Dendritic Processing to solve classify retinal images.The performance is competitive compared with common approaches like Support Vector Machines and Multilayer Perceptrons.The Lattice Neural Network with Dendritic Processing does not require the adjustment of parameters by the user.

Social networks are ubiquitous in everyday life. Although commonly analyzed from a perspective of individual interactions, social networks can provide insights about the collective behavior of a community. It has been shown that changes in the mood of social networks can be correlated to economic trends, public demonstrations, and political reactions, among others. In this work, we study community resilience in terms of the mood variations of the community. We have developed a method to characterize the mood steady-state of online social networks and to analyze how this steady-state is affected under certain perturbations or events that affect a community. We applied this method to study community behavior for three real social network situations, with promising results.

Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is crucial for developing rehabilitation andmotor assisted devices based on brain-computer interfaces (BCI). Here we consider the challenge of learning a classifier, basedon relevant patterns of the EEG signals; this learning step typically involves both feature selection, as well as a base learningalgorithm. However, in many cases it is not clear what combination of these methods will yield the best classifier. This papercontributes a detailed assessment of feature selection techniques, viz. , squared Pearson’s correlation (R 2 ), principal componentanalysis (PCA), kernel principal component analysis (kPCA) and fast correlation-based filter (FCBF); and the learning algorithms:linear discriminant analysis (LDA), support vector machines (SVM), and Feed Forward Neural Network (NN). A systematicevaluation of the combinations of these methods was performed in three two-class classification scenarios: rest vs. movement,upper vs. lower limb movement and right vs. left hand movement. FCBF in combination with SVM achieved the best results witha classification accuracy of 81.45%, 77.23% and 68.71% in the three scenarios, respectively. Importantly, FCBF determines, basedon the feature set, whether a classifier can be learned, and if so, automatically identifies the subset of relevant and non-correlatedfeatures. This suggests that FCBF is a powerful method for BCI systems based on MI. Knowledge gained here about proceduralcombinations has the potential to produce useful BCI tool, that can provide effective motor control for the users.

Radial-Basis Function Neural Networks (RBFN) are a well known formulation to solve classification problems. In this approach, a feedforward neural network is built, with one input layer, one hidden layer and one output layer. The processing is performed in the hidden and output layers. To adjust the network for any given problem, certain parameters have to be set. The parameters are: the centers of the radial functions associated to the hidden layer and the weights of the connections to the output layer. Most of the methods either require a lot of experimentation or may demand a lot of computational time. In this paper we present a novel method based on a partition algorithm to automatically compute the amount and location of the centers of the radial-basis functions. Our results, obtained by running it in seven public databases, are comparable and even better than some other approaches.

The identification of each phase in the process of movement arms from brain waves has been studied using classical classification approaches. Identify precisely each movement phase from relaxation to movement execution itself, is still an open challenging task. In the context of Brain-Computer Interfaces (BCI) this identification could accurately activate devices, giving more natural control systems. This work presents the use of a novel classification technique Lattice Neural Networks with Dendritic Processing (LNNDP), to identify motor states using electroencephalographic signals recorded from healthy subjects, performing self-paced reaching movements. To evaluate the performance of this technique 3 bi-classification scenarios were followed: (i) relax vs. intention, (ii) relax vs. execution, and (iii) intention vs. execution. The results showed that LNNDP provided an accuracy of (i) 65.26%, (ii) 69.07%, and (iii) 76.71% in each scenario respectively, which were higher than the chance level.

Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-relatedαandβfrequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.

EEG-based BCIs rely on classification methods to recognize the brain patterns that encode user's intention. However, decoding accuracies have reached a plateau and therefore novel classification techniques should be evaluated. This paper proposes the use of Lattice Neural Networks with Dendritic Processing LNND for the classification of hand movements from electroencephalographic EEG signals. The performance of this technique was evaluated and compared with classical classifiers using EEG signals recorded form participants performing motor tasks. The result showed that LNND provides: i the higher decoding accuracies in experiments using one electrode $$DA=80\,\%$$DA=80% and $$DA=80\,\%$$DA=80% for classification of motor execution and motor imagery, respectively; ii distributions of decoding accuracies significantly different and higher than the chance level $$p

DNA microarrays are being used to characterize the genetic expression of several illnesses, such as cancer. There has been interest in developing automated methods to classify the data generated by those microarrays. The problem is complex due to the availability of just a few samples to train the classifiers, and the fact that each sample may contain several thousands of features. One possibility is to select a reduced set of features genes. In this work we propose a wrapper method that is a modified version of the Inertial Geometric Particle Swarm Optimization.We name it MIGPSO. We compare MIGPSO with other approaches. The results are promising. MIGPSO obtained an increase in accuracy of about 4i¾?%. The number of genes selected is also competitive.

In this paper we consider a motion planning problem that occurs in tasks such as spot welding, car painting, inspection, and measurement, where the end-effector of a robotic arm must reach successive goal placements given as inputs. The problem is to compute a nearoptimal path of the arm so that the end-effector visits each goal once. It combines two notoriously hard subproblems: the collisionfree shortest-path and the traveling-salesman problems. It is further complicated by the fact that each goal placement of the end-effector may be achieved by several configurations of the arm (distinct solutions of the arm's inverse kinematics). This leads to considering a set of goal configurations of the robot that are partitioned into groups. The planner must compute a robot path that visits one configuration in each group and is near optimal over all configurations in every goal group and over all group orderings. The algorithm described in this paper operates under the assumption that finding a good tour in a graph with edges of given costs takes much less time than computing good paths between all pairs of goal configurations from different groups. So, the algorithm balances the time spent in computing paths between goal configurations and the time spent in computing tours. Although the algorithm still computes a quadratic number of such paths in the worst case, experimental results show that it is much faster in practice.

The definition of the concept of S2-Enterprise (Smart x Sensing) reference model is presented. The S2 Reference Model (S2-RM) is targeted to assist in the system specification and development of future computer based systems, which support the creation of this concept. It is a hybrid reference model that includes Enterprise Integration Engineering Concepts and Interoperability models. The Reference Model for Open Distributed Processing or RM-ODP is used as the basis to define S2-RM because it allows the definition of the structure and characteristic of the Smart and Sensing systems in terms of five different views (enterprise, information, computational, engineering and technology). The importance of using reference models for the definition, design and implementation of S2-Enterprise systems is outlined. A pilot system has been developed using a micro-factory concept and a collaborative networked organization.

Diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) molecular subtype. These entities are driven by different intracellular oncogenic signaling pathways that lead to a distinct clinical outcome (Fang, Xu, & Li, 2010; Lenz et al., 2008). Several immunohistochemical (IHC)-based DLBCL classification algorithms have been proposed, this considers the case when genetic expression profile (GEP) studies are not available. However, there is a major discrepancy within IHC algorithms, and when they are compared to GEP (Coutinho et al., 2013). To address these inconsistencies and determine if an automatic classifier could be used to accurately categorize DLBCL subtype, we perfomed a present a performance comparison between eight reported IHC algorithms (Colomo, Hans, Hans modified [Hans*], Nyman, Choi, Choi modified [Choi*] and Visco-Young with three [VY3] and four [VY4] antibodies) against their counterparts developed by automatic classification techniques, which consider the following structures: Bayesian Classifier (B), Bayesian Simple Classifier (BS), Naïve Bayesian Classifier (BN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). The Visco-Young database (Visco et al., 2012), which contains GEP, IHC raw data corresponding to GCET1, MUM1, FOXP1, BCL6, and CD10 antibodies, and clinical information of 475 de novo DLBCL patients, was used. According to GEP, the database contained 231 GCB, and 244 non-GCB cases. Each patient in VY database was ranked by survival rate as: low survival (0 - 34 months, 237 patients), medium survival (35 - 69 months, 173 patients), or high survival (70 - 106 months, 65 patients) rate. For the implementation of automatic classifiers, the database was split into training, testing and validation data subsets (75%, 20% and 5% respectively) by random selection, but to preserve the same proportion of ranked patients, the so-called k-fold cross-validation technique was applied. The automatic classifier versions of IHC algorithms used the same raw IHC data (antibody combination) as the input, e.g. VY3 used CD10, FOXP1, and BCL6 raw IHC as well as the ANN VY3. A total of 35 automatic classifiers were trained, where Colomo and Hans use the same set of antibodies and are represented by the same automatic classifiers. The stopping criterion during the training stage for all classification algorithms was an error less than 1x10-3 or 100 training epochs, whichever was satisfied first. The performance of the eight IHC algorithms and the automatic classifiers was evaluated by computing the accuracy (Acc), specificity (Spec), and sensitivity (Sens), according to the Receiver Operating Characteristic procedure. Five classifiers obtained the highest metrics: ANN Choi, BS Choi, and BS Choi* with 94.2% Acc, 93.1% Spec, and 95.2% Sens, followed by SVM Choi and SVM VY4 with 94.2% Acc, 91.4% Spec, and 96.8% Sens. Choi was the IHC algorithm with better metrics (92.5% Acc, 84.5% Spec, and 100% Sens), which ranked 11 out of 43 models tested, followed by VY3 and VY4 (ranked 22 and 23, respectively). Survival of GCB and non-GCB groups identified by these models were compared using Kaplan-Meier curves, and the significance was calculated using log-rank test. For the best five automatic classifiers and the Choi IHC algorithm, GCB overall survival was better than non-GCB cases (p < 0.05). To statistically compare the models with GEP, all automatic classifiers and IHC algorithms results were analyzed by Cohen's kappa (κ) for agreement analysis and Pearson's chi-squared test. Only Choi IHC algorithm had a very good agreement when compared with GEP (κ = 0.85, p < 0.001). The best five automatic supervised classifiers provided a perfect agreement with GEP (κ = 0.88, p < 0.001). Moreover, the agreement between IHC algorithms was mainly from moderate to good (κ: 0.41 - 0.79), except for Choi having a very good agreement with both VY3 and VY4 (κ = 0.95, p < 0.001). Conversely, a very good agreement within supervised classifiers was observed (κ: 0.77 - 1.00). Harnessing all of the available immunohistochemical data in order to increase the DLBCL classification accuracy when compared with decision three pre-existing algorithms, we conclude that 4 antibody-based BS Choi* automatic classifier provided the best metrics and represents an affordable and time-saving alternative for DLBCL molecular subtype identification. Disclosures No relevant conflicts of interest to declare.

Electro-mechanical meters are commonly employed to measure the consumption of utilities. Basically there exist two types of analog meters: the ones that use rotary dials (like an odometer) and the ones with pointer dials (like a speedometer). Former approaches to automated meter reading have dealt with the first kind of meters. Considering that automated reading of the latter ones can be confusing, in this work we introduce a methodology based on image processing and segmentation to enable the image acquisition and processing of pointer dials to obtain efficiently and accurately readings. This methodology uses an image acquired with a smart phone and by applying a sequence of image processing functions it finds and extracts the dial images of such meter images. Then the methodology identifies the position of the pointers followed by a clever implementation that enables the reading. The database is composed with more than a hundred images taken under different light conditions, perspectives and angles. The method is able to extract the reading in an average of 3 seconds, with a 92 % accuracy with images taken in-field. Our method, enables the use of a common smart phone to acquire and automatically extract the reading of a pointer-type dial meter. This allows interesting applications that could help people to monitor their energy consumption and learn patterns to save energy. This could be one step ahead of energy saving policies that can be discovered through massive data analysis.

Blood vessel segmentation is the first step in the process of automated diagnosis of cardiovascular diseases using retinal images. Unlike previous work described in literature, which uses rule-based methods or classical supervised learning algorithms, we applied Lattice Neural Networks with Dendritic Processing (LNNDP) to solve this problem. LNNDP differ from traditional neural networks in the computation performed by the individual neuron, showing more resemblance with biological neural networks, and offering high performance on the training phase (99.8% precision in our case). Our methodology requires four steps: 1)Preprocessing, 2)Feature computation, 3)Classification, 4)Postprocessing. We used the Hotelling T 2 control chart to reduce the dimensionality of the feature vector from 7 to 5 dimensions, and measured the effectiveness of the methodology with the F 1 Score metric, obtaining a maximum of 0.81; compared to 0.79 of a traditional neural network.

Planning the motion of a rigid or articulated object among static obstacles is known as the basic motion planning problem. In its simplest definition, it is a purely geometric problem that only considers avoiding collision with static obstacles, given that the robot is the only object that is able to move in the environment. This problem has been extensively studied in the last two decades, and we do know that the problem itself can be very challenging. For instance, it has been demonstrated that planning the motion of a set of polyhedral objects forming an articulated structure in an environment with static polyhedral obstacles is PSPACE-hard (Reif, 1979). That means that although some elegant complete algorithms exist –the most efficient complete and general algorithm for basic motion planning has a O(2n) complexity (Canny, 1988)-their prohibitive computational cost has motivated the search for efficient algorithms that can run in significantly less time despite the fact that they are not complete.

Flock traffic navigation based on negotiation (FTN) is a new approach for solving traffic congestion problems in big cities. Early works suppose a navigation path based on a bone-structure made by initial, ending and geometrical intersection points of two agents and their rational negotiations. In this paper we present original methods based on clustering analysis to allow other agents to enter or abandon flocks according to their own self interests.

Free-space dilation is an effective approach for narrow passage sampling, a well-recognized difficulty in probabilistic roadmap (PRM) planning. Key to this approach are methods for dilating the free space and for determining the amount of dilation needed. This paper presents a new method of dilation by shrinking the geometric models of robots and obstacles. Compared with existing work, the new method is more efficient in both running time and memory usage. It is also integrated with collision checking, a key operation in PRM planning. The efficiency of the dilation method enables a new PRM planner which quickly constructs a series of dilated free spaces and automatically determine the amount of dilation needed. Experiments show that both the dilation method and the planner work well in complex geometric environments. In particular, the planner reliably solved the most difficult version of the alpha puzzle, a benchmark test for PRM planners

A number of advanced sampling strategies have been proposed in recent years to address the narrow passage problem for probabilistic roadmap (PRM) planning. These sampling strategies all have unique strengths, but none of them solves the problem completely. In this paper, we present a general and systematic approach for adaptively combining multiple sampling strategies so that their individual strengths are preserved. We have performed experiments with this approach on robots with up to 12 degrees of freedom in complex 3-D environments. Experiments show that although the performance of individual sampling strategies varies across different environments, the adaptive hybrid sampling strategies constructed with this approach perform consistently well in all environments. Further, we show that, under reasonable assumptions, the adaptive strategies are provably competitive against all individual strategies used.

This paper presents some preliminary results on the application of a Cooperative Simulated Annealing algorithm (COSA) for solving the problem of path planning in Multi-Robot systems. The main idea is to generate paths for each robot in the system without taking care of the other robots, and then coordinate the paths for all the robots. Paths are generated using a variation of the probabilistic roadmaps (PRM), and the coordination of the paths is achieved with a Cooperative Simulated Annealing (COSA). To evaluate the system, several experiments for environments constituted by two planar robots with up to five dof and various convex obstacles were run. The results obtained aim to continue working on this line.

DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. In this paper, we propose a new methodology that is based on the application of Principal Component Analysis and other statistical tools to gain insight in the identification of relevant genes. We run the approaches using two benchmark datasets: Leukemia and Lymphoma. The results show that it is possible to reduce considerably the number of genes while increasing the performance of well known classifiers.

This paper considers the following multi-goal motion planning problem: a robot arm must reach several goal configurations in some sequence, but this sequence is not given. Instead, the robot's planner must compute an optimal or near-optimal path through the goals. This problem occurs, for instance, in spot-welding, inspection, and measurement tasks. It combines two computationally hard sub-problems: the shortest-path and traveling-salesman problems. This paper describes a greedy algorithm that operates under the assumption that the number of goals is relatively small (a few dozen at most) and the computational cost of finding a good path between two goals dominates that of finding a good tour in a graph with edges of given costs. Although the algorithm computes a quadratic number of goal-to-goal paths in the worst case, it is much faster in practice.